“Social Customer” Segmentation: Case Study in Tablet Market

Marketers used market segmentation methods for a very long time. However, as our abilities to collect and manage information continues to improve, the new methods of segmentation become available to enable more targeted marketing efforts for marketers and better products and services for consumers.

One of the most commonly accepted strategies utilized is demographic segmentation based on an assumption that a specific group (based on age, gender, etc) is a primary consumer of your product or service. Sometimes this assumption is based on the product purchase history. Regardless of the validity of an assumption, it does not often provide an insight on “WHY” this demographic segment would select the product in question or “HOW” they would use it. In other words, there is a lot of guessing that has to take place or additional segmentation strategies to be deployed. In my opinion, the popularity of demographic strategy lay mostly in its low cost and ease of access as behavioral and psychographic segmentation requires a lot of research that translates into high cost and time-to-market constraints.

Segmenting social customers by interest

The advances in technology start to offer new opportunities for market segmentation based on automated analysis of customer-generated content which is becoming available with the proliferation of social media and the rise of Social Consumer. Essentially instead of assuming what demographic group would be the ideal target for our marketing efforts, we could look at a group that already expressed their interest by purchasing specific types of products or services and learn “WHAT” elements of their experience were important to them.

Most companies of any size use online survey techniques in an attempt to engage their customers, but the method does not support discovery of customer perspective; it validates assumptions of the company based on questions posed and deemed important. Again, the primary driver of survey method popularity is not the quality of the output and ability to provide better market intelligence, but the cost of implementation.

I would suggest there are better alternatives today to learn unbiased market segment knowledge in applications of Opinion Mining technology to unsolicited customer-generated content. This approach can offer quality market segment intelligence that rivals surveys in terms of implementation complexity and cost.

Analysis of tablet market

To illustrate my point, let’s look at tablets market segment defined by a few popular products in this category; however, non-like products that compete for the same wallet share can be used to get valuable insights:

Apple iPad2 (666 customer “stories”)

Blackberry Playbook (255)

HP TouchPad (650)

Motorola Xoom (576)

Samsung Galaxy Tab 10.1 inch and (502)

Toshiba Thrive (433)

These products were selected based on their popularity that manifested itself in a number of their customer-generated content references available online in a form of customer reviews, forum comments or social networks product page messages.

The first level of Customer Intelligence gained by Opinion Mining of this customer content is a list of customer experience attributes, sorted by their importance. The importance is measured as a percentage of total number of unsolicited opinions expressed by the customers. This answers the questions – WHAT is important to the customers and HOW important that is.

The next level allows the measuring of the difference between customer expectations and their experience and measures HOW well the customers’ needs are met. We use a two-point scale to visualize that difference (0=unacceptable, 1=experience meets expectations, 2=delighted); however, the measurements can easily be converted to any scale of choice without losing their meaning or accuracy. The chart below focuses on the top four attributes of customer experience by their importance to illustrate the approach.

There are practical implications of these measurements as they reflect on marcomm messaging that have created customer expectations the product needs to meet. In the example above, most of the products exceeded the expectations of their customers in attributes most important to them by a significant margin. As an illustration, I would suggest that perhaps messaging about usability of these products could leverage customer sentiment to assure consumers who are hesitant to make a purchase and increase their products market adoption. That calls for a next level of intelligence that provides an answer to WHY customers feel this way and provide a context in which they express their opinions.

Above is a very small sample to illustrate the use of words and expressions (in square brackets) people to describe their opinions, and how they are attributed to a specific element of customer experience.

These words, expressions ad even quotes can be used to fortify marketing messaging. Think of the very successful marketing campaign by Tempur-Pedic.

The flip side of the coin – early understanding of root causes of customer disappointment – can help to alleviate larger problems, turn the problem situation around or even present an opportunity for differentiation as illustrated below.

Looking deeper reveals a lot of unhappiness about compatibility:

And even deeper analysis will provide a context that is invaluable for taking an advantage of the opportunity:

As many marketers are looking for intimate knowledge of customer’s perception of their brands, the value of this type of market intelligence should not be discounted, as it can be easily produced at low cost.

Gregory Yankelovich is a Technologist who is agnostic to a technology, but "religious" about Customer Experience and ROI. He has solid experience delivering high ROI projects with focus on both Profitability AND Customer Experience improvements, as one without another does not support long term business growth. Gregory currently serves as the Customer Experience (CX) Whisperer at Customer Experience IQ (formerly Amplified Analytics), provider of a Social Media Research service focused on mining Customer Experience Intelligence for support of Product Marketing and Management.

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